As part of our ongoing ‘It’s all about data’ series, I am exploring the many challenges that the Private Credit space faces. When it comes to the management of data itself, I find this one of the most challenging to write, because the data challenge for Private Credit managers is one of the hardest to address.
So what makes the private credit space so challenging?
Private Credit managers are always in flux; adapting to the changing dynamics of the market, with the need for investors to not only understand performance, but also have the transparency they desire in understanding the nature of risk related to the investments themselves. As one ODD professional stated, “Private Credit is as yet untested in a full economic cycle, especially the more esoteric strategies”. Wise words indeed.
Predominantly coming from the Private Equity side of alternatives, many Private Credit managers are still capturing the deal terms and conditions & covenants through manual processes and trawling through vast quantities of documents while utilising our old friend, MS Excel, to capture that data.
It’s clear to us here in the Fintech arena that Private Credit managers are behind the Hedge Fund players in terms of embracing technology to automate deal workflows and data capture. In a recent roundtable with Private Credit CFOs and COOs, front office technology adoption was cited to be a major challenge in the pursuit of a more seamless data experience across firms; something the industry will have to embrace with increasing assets and deal flow of opportunities being presented. Even the broadly syndicated (bank debt) market has played ‘catch up’ and has entered the 21st century with the flow of bank-related data.
However, the nature of our business is targeting mid-market private companies, who’s financial health needs to be captured and married with the terms of the associated deals. Investors are seeking an understanding of the valuations of underlying assets and company performance, to understand the risk profiles in continued uncertain times. While performance had demonstrated resilience, all are aware there are still unknowns when they embrace the asset class.
The financial performance of the underlying portfolio companies was mooted as one of the most challenging aspects of the business, following a recent Private Credit CFO/COO roundtable that I hosted. There are partial solutions, but the pursuit of an automated and accurate way of pulling in unstructured datasets remains a key objective here at Portfolio BI. This will be the ‘holy grail’ of solutions and will create a more robust access to this data in a structured capacity.
The reality is that the more manual and disparate the data workflow and sources are, the less scalable the managers business will become, which was a lesson learned over a decade ago in the traditional Hedge Fund arena. Private Credit managers need to think longer term, and build a roadmap for the business they will be in 5-10 years, with more assets, higher competition (from banks and peers), more complex deals, and higher investor reporting requirements.
So how should firms address their data?
- A holistic and seamless overview of data is essential: Technology and data cannot be approached in a piecemeal manner. It’s important for managers to step back and look at the entire data architecture of their business, the taxonomy, where specific systems have key strengths, ensuring ‘customisation’ doesn’t push them far from the standard functionality, and build for flexibility. In a recent article by Gary Maier*, Managing Principal of Fintova LLC, industry technology leader and Private Credit veteran (Blackrock, BNY Mellon, UBS), this point was addressed in the following way; “A robust data architecture and data pipeline that can capture, normalise, curate, enrich, govern, and distribute a wide range of both financial and non-financial data sources.” He then went on to cite a critical goal for firms: “An integration fabric that can ensure a seamless flow of data and processes across both in-house and third-party systems, functions, and processes” is a priority.
- Data governance is essential: Importantly, data governance is key in ensuring these systems work. Capturing data is one component, but validating its accuracy without the burden of excessive exception management is essential.
- Embracing the potential of AI: The former points signal a huge opportunity for AI-based solutions, yet this more elusive than you think. It’s also important to address the fact that even mature managers in the space are in process of an operational and technology flux, adapting legacy systems with new technologies to provide for more seamless data analytics.
In later instalments, we will address the question of AI, but marrying various data sets together and creating a solution that is adaptable for the future is already being addressed in a multitude of ways. There is a lot to think about and together, as an industry, we will move forward to ignite the next generation solution of data management.
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